Data composite for efficient memory transfer in a behavioral recognition system

ABSTRACT

Techniques are disclosed for dynamic memory allocation in a behavioral recognition system. According to one embodiment of the disclosure, one or more variable-sized chunks of memory is allocated from a device memory for a memory pool. An application allocates at least one of the chunks of memory from the memory pool for processing a plurality of input data streams in real-time. A request to allocate memory from the memory pool for input data is received. Upon determining that one of the chunks is available in the memory pool to store the input data, the chunk is allocated from the memory pool in response to the request.

BACKGROUND Field

Embodiments of the present disclosure generally relate to techniques foranalyzing digital images. More specifically, embodiments presentedherein provide a framework for processing large amounts of data at arelatively high rate.

Description of the Related Art

Computer systems, in addition to standard processing resources of acentral processing unit (CPU), may use computing resources provided by agraphics processing unit (GPU) to process large amounts of data inreal-time. That is, although systems typically use GPUs to rendergraphics for display, some GPUs allow an application to use the parallelcomputing capabilities provided by the CPU to improve performance of theapplication.

For example, a behavioral recognition system configured to analyze videostreams may receive and process data from a number of input sources inreal-time. Such data may include video data at different resolutions,and therefore various sizes. Further, the behavioral recognition systemmay process the video data in different phases (e.g., foreground andbackground differentiation, object detection, object tracking etc.), andsuch processing requires considerable resources. To improve performance,the behavioral recognition system may use the parallel processingcapabilities provided by the GPU. For example, the behavioralrecognition system may allocate memory in the GPU so that the CPU maytransfer video data to the GPU. Doing so allows the behavioralrecognition system to push processing tasks to the GPU while the CPUconcurrently performs its own processing tasks.

However, using GPU to process data has several limitations. Forinstance, a memory allocation in the GPU is a synchronizing event. Thatis, while the GPU is allocating memory, other GPU processes (e.g.,kernel execution, registrations, etc.) are suspended until the memory isallocated. Another example is that GPUs typically limit the amount ofmemory transfers between host CPU and device GPU, e.g., onebidirectional transfer at a time. As a result, the transfer limit canstifle the rate that data is sent between host and device, hindering theability of the behavioral recognition system to analyze data in a timelymanner.

SUMMARY

One embodiment presented herein discloses a method. This methodgenerally includes allocating, from a device memory, one or morevariable-sized chunks of memory for a memory pool. An applicationallocates at least one of the chunks of memory from the memory pool forprocessing a plurality of input data streams in real-time. This methodalso generally includes receiving a request to allocate memory from thememory pool for input data. Upon determining that one of the chunks isavailable in the memory pool to store the input data, the chunk isallocated from the memory pool in response to the request.

Other embodiments include, without limitation, a non-transitorycomputer-readable medium that includes instructions that enable aprocessing unit to implement one or more aspects of the disclosedmethods as well as a system having a processor, memory, and applicationprograms configured to implement one or more aspects of the disclosedmethods.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features, advantages, andobjects of the present disclosure are attained and can be understood indetail, a more particular description of the disclosure, brieflysummarized above, may be had by reference to the embodiments illustratedin the appended drawings.

It is to be noted, however, that the appended drawings illustrate onlytypical embodiments of the present disclosure and are therefore not tobe considered limiting of its scope, for the present disclosure mayadmit to other equally effective embodiments.

FIG. 1 illustrates an example computing environment, according to oneembodiment.

FIG. 2 further illustrates components of the server computing systemshown in FIG. 1, according to one embodiment.

FIG. 3 illustrates an example server computing system configured toprocess a large amount of data in real-time, according to oneembodiment.

FIG. 4 illustrates an example data processing pipeline, according to oneembodiment.

FIG. 5 illustrates an example of processing phase data, according to oneembodiment.

FIG. 6 illustrates a method for dynamically allocating memory viaCPU-side and GPU-side memory pools, according to one embodiment.

FIG. 7 illustrates a method for freeing (deallocating) memory in amemory pool, according to one embodiment.

FIG. 8 illustrates an example of preparing a composite of multiple feedsof data for transfer between host and device, according to oneembodiment.

FIG. 9 illustrates a method for preparing a composite of multiple feedsof data for transfer between host and device, according to oneembodiment.

DETAILED DESCRIPTION

Embodiments presented herein disclose techniques for managing memory ina computer system configured to process a large amount of data inreal-time. For example, embodiments presented herein may be adapted to abehavioral recognition system that receives and analyzes real-time data(e.g., video data, audio data, SCADA data, and so on). A data driver(e.g., a video driver, an audio driver, a SCADA driver) in thebehavioral recognition system may process data at various input sensorsin a succession of phases, where the output of the final phase is usedto analyze the data, e.g., learning a pattern of behavior that isnormal, such that the system can later identify anomalous behaviorobserved in subsequently observed real-time data.

In one embodiment, to achieve optimal performance, the data driver isconfigured as part of a high data rate (HDR) framework that usesparallel computing capabilities of a graphics processing unit (GPU). TheHDR framework may organize phases for each input sensor into aprocessing pipeline. The CPU and GPU may process a copy of data inmemory in each pipeline in parallel while transferring data between oneanother.

The GPU may be subject to several memory management limitations thataffect performance of a system. For example, device memory allocation istypically a synchronizing event. Consequently, other processes occurringin the GPU are suspended until the GPU has completed allocating memory.As another example, the GPU, due to hardware restrictions, may beallowed a limited amount of memory transfers at a time, e.g., onetransfer from host-to-device (and vice versa) at a time.

In one embodiment, to address such limitations, the data driver isconfigured to dynamically manage memory that is allocated in the hostCPU and GPU device. In particular, the data driver maintains memorypools in the behavioral recognition system in host-side memory anddevice-side memory. The host-side memory pool may comprise pinnedmemory, and the device-side memory may comprise memory in the GPU. Inone embodiment, a memory management component in the data driverallocates memory for use in memory pools of the CPU and the GPU. Inparticular, the data driver may allocate chunks of different sizes.Doing so allows the behavioral recognition system to accommodate variousdata (e.g., video files of different resolutions) at a time. Othercomponents in the data driver may check out blocks of memory from thememory pools as needed. And when the memory is no longer needed, thecomponents may check the blocks back in to the memory pool. Further, thedata driver may release unused memory chunks from a given memory poolbased on a decay time constant measure. When released from the memorypool, the memory chunks become available for future allocation (e.g.,for allocation back to the memory pool as needed, or for allocation byprocesses other than the data driver).

Using memory pools to provide dynamic memory allocation by the datadriver improves performance of the behavioral recognition system inlight of memory constraints of a GPU device, which typically hassignificantly less memory than a CPU (e.g., a CPU may have 128 GBmemory, whereas a GPU may have 6 GB of memory). In one embodiment, toavoid excess dormant memory blocks allocated in the memory pool (andthus avoid choking due to unused allocated memory), the data driver mayallocate memory in multiples of N rows and N columns (e.g., if N=128,the data driver may allocate a 384×256 chunk of memory to for a videofeed frame having a 352×240 resolution).

In addition, to reduce the amount of necessary memory transfers in eachprocessing pipeline, the data driver may composite data from multipleinput sensors before memory containing the data is transferred betweenhost and device. To do so, the data driver may perform a bin-packingalgorithm for incoming data from a number of input sensors. Using a datadriver that processes video surveillance feeds as an example, datadriver may pack a number of video feeds of varying resolutions and framerates to prepare a reasonably-sized composite based on individualframe-rates, packing largest video feeds first and as closely aspossible to efficiently use a memory chunk.

Once the composite data (e.g., a composite of video frames received at agiven instance) is generated, the data driver may initiate transfer fromhost to device. Both the host and device may then process the data ateach stage in parallel. For example, the data driver may processhost-side data per feed, whereas the data driver processes device-sidedata per feed within the composite itself. And because the host anddevice are working on the same copy of the composite, the number ofoverall transfers between the host and device are reduced within thepipeline, thus increasing performance.

Note, the following uses a behavioral recognition system that adaptivelylearns patterns of activity from various types of data (e.g., videodata, raw image data, audio data, SCADA data, information security data,etc.) as an example of a system that receives and analyzes relativelylarge amounts of data in real-time. However, one of skill in the artwill recognize that embodiments disclosed herein are adaptable to avariety of systems configured with a GPU that is enabled to allowapplications to use its parallel computing capabilities for processinglarge amounts of data in real-time (or within a short time frame). Forexample, embodiments may also be adapted towards big data systems thatexecute Extract, Transform, and Load (ETL) workflows.

FIG. 1 illustrates a computing environment 100, according to oneembodiment. As shown, computing environment 100 includes source devices105, a network 110, a server system 115, and a client system 130. Thenetwork 110 may transmit streams of data (e.g., video frames) capturedby one or more source devices 105 (e.g., video cameras installed atvarious locations of a facility, etc.). Of course, the source devices105 may be connected to the server system 115 directly (e.g., via USB orother form of connecting cable). Network 110 the data streams from thesource devices 105 in real-time. In addition to a live feed provided bythe source device 105, the server system 115 could also receive a streamof video frames from other input sources (e.g., VCR, DVR, DVD, computer,web-cam device, and the like). Video frames from a given source device105 could have a different resolution compared to video frames fromanother source device 105.

For example, the source devices 105 may be video cameras situated atvarious locations in a building or facility. For example, source devices105 may be situated in a parking garage to capture video streams atthose locations. Each camera may provide streaming feed (i.e., acontinuous sequence of images, or frames) analyzed independently by theserver system 115. The source devices 105 may be configured to capturethe video data as frames at a specified frame-rate. Further, the videodata may be encoded using known formats, e.g., JPEG, PNG, GIF, and thelike.

In one embodiment, the server system 115 includes a data driver 120 anda machine learning engine 125. In one embodiment, the server system 115represents a behavioral recognition system. As further described below,data driver 120 processes the streams of data sent from the sourcedevices 105 through a single- or multi-feed pipeline. In one embodiment,the server system 115 provides a high data rate (HDR) framework thatallows, e.g., a developer, to adapt the data driver 120 to processvarious types of data, such as video data, audio data, image data, SCADAdata, and the like, in real-time.

The data driver 120 may process incoming data from the source devices105 using a pipeline that includes a number of phases. During eachphase, the data driver 120 may perform a given task and use theresulting data as input for a successive phase. For example, assume thatthe data driver 120 processes video data from source devices 105. Onephase within the pipeline may include analyzing a scene for foregroundand background data. Another phase may include detecting foregroundobjects. And another phase may include tracking the objects within thescene. The data driver 120 outputs processed data to the machinelearning engine 125.

In one embodiment, the machine learning engine 125 evaluates, observes,learns, and remembers details regarding events (and types of events)occurring within the data streams. When observations deviate fromlearned behavior (based on some learning model), the machine learningengine 125 may generate an alert (e.g., to a management console 135executing on the client system 130). In one embodiment, the machinelearning engine 125 performs neural-network-based linguistic analysis ofthe resulting data generated by the data driver 120.

The machine learning engine 125 generates a learning model by organizingthe processed data into clusters. Further, the neuro-linguistic modulemay assign a symbol, e.g., letters, to each cluster which reaches somemeasure of statistical significance. From the letters, theneuro-linguistic module builds a dictionary of observed combinations ofsymbols, i.e., words based on a statistical distribution of symbolsidentified in the input data. Specifically, the neuro-linguistic modulemay identify patterns of symbols in the input data at differentfrequencies of occurrence, up to a maximum word size (e.g., 5 letters).

The most frequently observed words (e.g., 20) provide a dictionary ofwords corresponding to the stream of data. Using words from thedictionary, the neuro-linguistic module generates phrases based onprobabilistic relationships of each word occurring in sequence relativeto other words, up to a maximum phrase length. For example, theneuro-linguistic module may identify a relationship between a giventhree-letter word that frequently appears in sequence with a givenfour-letter word, and so on. The syntax allows the machine learningengine 125 to learn, identify, and recognize patterns of behaviorwithout the aid or guidance of predefined activities.

Thus, unlike a rules-based system, which relies on predefined patternsto identify or search for in a data stream, the machine learning engine125 learns patterns by generalizing input and building memories of whatis observed. Over time, the machine learning engine 125 uses thesememories to distinguish between normal and anomalous behavior reflectedin observed data.

FIG. 2 further illustrates the server system 115, according to oneembodiment. As shown, the server system 115 further includes a sensormanagement module 205 and a sensory memory 215. In addition, the machinelearning engine 125 further includes a neuro-linguistic module 220 and acognitive module 225. And the sensor management module 205 furtherincludes a sensor manager 210 and the data driver 120.

In one embodiment, the sensor manager 210 enables or disables sourcedevices 105 to be monitored by the data driver 120 (e.g., in response toa request sent by the management console 135). For example, if themanagement console 135 requests the server system 115 to monitoractivity at a given location, the sensor manager 210 determines thesource device 105 configured at that location and enables that sourcedevice 105.

In one embodiment, the sensory memory 215 is a data store that transferslarge volumes of data from the data driver 120 to the machine learningengine 125. The sensory memory 215 stores the data as records. Eachrecord may include an identifier, a timestamp, and a data payload.Further, the sensory memory 215 aggregates incoming data in atime-sorted fashion. Storing incoming data from the data driver 120 in asingle location allows the machine learning engine 125 to process thedata efficiently. Further, the server system 115 may reference datastored in the sensory memory 215 in generating alerts for anomalousactivity. In one embodiment, the sensory memory 215 may be implementedin via a virtual memory file system. In another embodiment, the sensorymemory 215 is implemented using a key-value pair.

In one embodiment, the neuro-linguistic module 220 performs neuralnetwork-based linguistic analysis of normalized input data to describeactivity observed in the data. As stated, rather than describing theactivity based on pre-defined objects and actions, the neuro-linguisticmodule 220 develops a custom language based on symbols, e.g., letters,generated from the input data. The cognitive module 225 learns patternsbased on observations and performs learning analysis on linguisticcontent developed by the neuro-linguistic module 220.

FIG. 3 further illustrates the server system 115, according to oneembodiment. As shown, the server system 115 includes, withoutlimitation, a central processing unit (CPU) 305, a graphics processingunit (GPU) 306, a network interface 315, a memory 320, and storage 330,each connected to an interconnect bus 317. The server system 115 mayalso include an I/O device interface 310 connecting I/O devices 312(e.g., keyboard, display and mouse devices) to the server system 115.Further, in context of this disclosure, the computing elements shown inserver system 115 may correspond to a physical computing system. In oneembodiment, the server system 115 is representative of a behavioralrecognition system.

The CPU 305 retrieves and executes programming instructions stored inmemory 320 as well as stores and retrieves application data residing inthe memory 330. The interconnect bus 317 is used to transmit programminginstructions and application data between the CPU 305, I/O devicesinterface 310, storage 330, network interface 315, and memory 320.

Note, CPU 305 is included to be representative of a single CPU, multipleCPUs, a single CPU having multiple processing cores, and the like. Andthe memory 320 is generally included to be representative of a randomaccess memory. The storage 330 may be a disk drive storage device.Although shown as a single unit, the storage 330 may be a combination offixed and/or removable storage devices, such as fixed disc drives,removable memory cards, optical storage, network attached storage (NAS),or a storage area-network (SAN).

In one embodiment, the GPU 306 is a specialized integrated circuitdesigned to accelerate graphics in a frame buffer intended for output toa display. GPUs are very efficient at manipulating computer graphics andare generally more effective than general-purpose CPUs for algorithmswhere processing of large blocks of data is done in parallel. As furtherdescribed below, the data driver 120 (and the machine learning engine125) uses the parallel processing capabilities of the GPU 306 to improveperformance in handling large amounts of incoming data (e.g., video datafrom numerous source devices 105) during each pipeline processing phase.

In one embodiment, the memory 320 includes the data driver 120, themachine learning engine 125, and an input image 326. And the storage 330includes alert media 334. As discussed above, the data driver 120processes input data 326 sent from source devices 105 for analysis bythe machine learning engine 125. The data driver 120 is customizable viaa high data rate (HDR) framework that allows a developer to configurethe data driver 120 to process a specified type of input data 326 (e.g.,video data, image data, information security data, or any type of datathat arrives to the data driver 120 in large amounts and needs to beprocessed in real-time). The machine learning engine 125 performsneuro-linguistic analysis on values that are output by the data driver120 and learns patterns from the values. The machine learning engine 125distinguishes between normal and abnormal patterns of activity andgenerates alerts (e.g., alert media 334) based on observed abnormalactivity.

As stated, the data driver 120 may use the parallel computingcapabilities provided by the GPU 306 to increase performance ofprocessing input data 326. In particular, a memory management componentin the data driver 120 may dynamically allocate variable-sized chunks ofmemory into host-side and device-side memory pools. Doing so allows thedata driver 120 to readily allocate memory for incoming data from thealready-allocated memory pool. That is, because device memory allocationin the GPU 306 is a synchronizing event (which blocks other GPUprocesses from being performed while the allocation occurs), the datadriver 120 allocates the data to the memory pools to avoid allocationsynchronization events during processing phases.

Further, the memory management component may allocate additional memorychunks into a given memory pool, as needed. Further still, to prevent anexcessive amount of dormant memory allocated to the memory pool (that istherefore unable to be allocated towards other processes in the serversystem 115), the memory management component may release unused memorychunks from the memory pool by applying a time decay constant towardsunused memory chunks in the memory pool. In addition, the memorymanagement component may be configured to restrict a specifiedpercentage of total memory (of host-side memory or of device-sidememory) that can be allocated to a memory pool at a given time.

Further, the data driver 120 may package blocks of input data 326 into acomposite copy that can be transferred to the device-side for processingby the GPU 306. Doing so allows the data driver 120 to use both threadprocesses in the CPU 305 and kernel processes in the GPU 306 to handlethe input data 326 during pipeline phases. Using video feed data as anexample, the data driver 120 may package multiple video frames fromdifferent sources and of different resolutions into as one memory block,e.g., using a bin-packing algorithm. The data driver 120 may allocatememory for the data from the memory pools.

FIG. 4 illustrates an example data processing pipeline of the datadriver 120 relative to memory pools in the server system 115, accordingto one embodiment. As shown, the pipeline includes multiple dataproviders 420, a composite phase 425, a phase 1 430, a phase 2 435, anda sample injection phase 440. Of course, the pipeline may includeadditional intermediary phases. Further, the server system 115 includesa pinned memory matrix pool 405 (allocated from CPU memory). The serversystem 115 further includes a GPU memory matrix pool 410 and a GPUmemory generic pool 415 (allocated from GPU memory). Note that inpractice, there are multiple pipelines based on host and device memory,number of data streams, and total number of source devices 105.

The pinned memory matrix pool 405 represents chunks of memory allocatedfrom pinned memory managed by the CPU 305. As known, pinned memoryremains in-place within the CPU RAM to facilitate data transfer to thememory of the GPU 306. The GPU memory matrix pool 410 includes memorychunks allocated in the GPU 306 memory that may be multi-dimensionalmatrices. The GPU memory generic pool 415 includes memory chunks thatare organized as memory blocks or arrays.

Illustratively, the data driver 120 may check out memory from each ofthe pools 405, 410, and 415. In one embodiment, a data provider 420connects with an assigned source device 105 and receives input data fromthe source device 105. The data provider 420 feeds the input data to thecomposite phase 425. In the video feed example, the composite phase 425may receive multiple frames originating from the various source devices105. In composite phase 425, the data driver 120 packages the multipleframes into a chunk of memory. At this phase 425, the data driver 120may check out memory from one of the GPU memory pools 410 and 415 forthe packaged data and transfer a copy of the packaged data to the GPU306. That is, rather than transfer data from a given data provider 420individually (and thus creating a performance bottleneck due to hardwarelimitations for transfers), the data driver 120 sends a composite ofdata received from the multiple data providers 420. Advantageously,doing so reduces the amount of data transfers needed between host anddevice.

In one embodiment, the data driver 120 analyzes the host-side dataseparately per data provider 420. Using video data as an example, thedata driver 120 analyzes host-side video streams on a per-feed basis,e.g., in the phase 1 430, phase 2 435, and/or the sample injection phase440. Further, the data driver 120 may analyze device-side video streamsper-feed but within the packaged data. As stated, in each phase, data isprocessed and then passed from one phase to another. The resulting datamay be sampled into values (e.g., from 0 to 1, inclusive) and output tothe machine learning engine 125 (via the sample injection phase 440).

FIG. 5 illustrates an example processing phase flow, according to oneembodiment. In particular, FIG. 5 depicts a phase 2 515 that hasreceived processed phase data 510 from a phase 1 505.

As an example, the phase 1 505 may correspond to a detector process thatdistinguishes foreground objects from background objects in a videofeed, and the phase data 510 may correspond to detected foreground andbackground objects. The phase 1 505 can output the resulting phase data510 to the phase 2 515. Phase 2 515 can include a process 520 thattracks each detected object within a series of video frames. The process520 may execute as a thread in a thread pool 525 (host-side) or within aprocess of a GPU kernel 530 (device-side) based on whether the phase 2515 is processing the feed within CPU memory or within GPU memory. Theprocess 525 can output phase 2 data 535 to a phase 3 540 for furtherprocessing.

FIG. 6 illustrates a method 600 for dynamically allocating memory viahost-side and device-side memory pools, according to one embodiment. Inthis example, assume that the data driver 120 previously allocatedmemory chunks in each of the memory pools of the CPU and the GPU. Themaximum amount of memory allocated in a given memory pool may be subjectto a specified configuration, e.g., x % of total memory in the CPU (orGPU).

As shown, method 600 begins at step 605, where a memory managementcomponent in the data driver 120 receives a request to allocate a chunkof memory for data having a specified size. For example, the memorymanagement component may receive the request from the composite phaseprocess to allocate pinned memory from the memory pool that is largeenough to store the composite data.

At step 610, the memory management component determines whether a chunkof memory that is large enough to contain the data is available in thememory pool. As stated, the chunks in a given memory pool may beallocated in multiples of N rows and N columns, e.g., N=128. To avoidexcess dormant memory blocks, the memory management component may selecta chunk that is slightly larger than the data in the request. Usingvideo feed data as an example, the phase may request memory from thepinned matrix memory pool for a SIF (source input format) frame of352×240 resolution. Assuming that N=128, the memory management componentmay determine whether a chunk of size 384×256 is available in the pinnedmemory pool.

If not, then at step 615, the memory management component allocates amemory chunk from available (i.e., not currently allocated) memory inthe CPU RAM or the GPU, based on the request. Otherwise, at step 620,the memory management component checks out and uses the memory chunkfrom the memory pool. In the event that the request is specified to theGPU, allocating memory from the memory pool avoids allocating availablememory in the GPU, thus avoiding a synchronizing event and allowingother processes in the GPU to continue executing.

The memory management component may continue to check memory chunks inand out of the memory pools as needed by the data driver 120. Further,in one embodiment, the memory management component may deallocate unusedmemory from the memory pools subject to a time decay constant. Doing sominimizes the amount of dormant memory allocated to a given memory pool.As known, dormant memory is generally undesirable because such memoryremains allocated to the pool yet unused by the data driver 120 and, atthe same time, unavailable to other processes executing in the serversystem 115.

FIG. 7 illustrates a method 700 for deallocating memory from a givenmemory pool, according to one embodiment. As shown, method 700 begins atstep 705, where the memory management component evaluates a chunk ofmemory in the memory pool that is currently not allocated to data. To doso, the memory management component may evaluate timestamps associatedwith the memory chunk that indicate the instance that the memory chunkwas most recently allocated to data.

At step 710, the memory allocation component determines whether theperiod that the memory chunk currently remains unallocated for aspecified amount of time. For example, the memory management componentmay do so using a time decay constant relative to the amount of timethat the memory chunk is unused. If not, then the method 700 ends.Otherwise, at step 715, the memory management component releases theunused memory chunk from the memory pool. The memory managementcomponent may reallocate the memory to the memory pool at a later pointin time (e.g., as demand for more memory from the processing phasesgrows).

The data driver 120 may allocate memory for data (e.g., video feeds andthe like) being analyzed in various phases. For example, FIG. 8illustrates a flow for preparing a composite of multiple feeds of datafor transfer between host (above the bold line in FIG. 8) and device,according to one embodiment. As stated, the data driver 120 receives, atmultiple data providers, a number of streams of data, such as videofeeds. As an example, FIG. 8 depicts each video feed 805 as a block ofdata in the host-side (CPU) memory of the server system 115. Each of thevideo feeds 805 may be of various resolutions. For example, one videofeed 805 could be at a 800×600 resolution, 1024×768 resolution, etc. Inaddition, each of the video feeds 805 may be of various frame rates.

To use the parallel processing capabilities of the GPU 306, the datadriver 120 needs to transfer a copy of the feeds 805 to device-sidememory. To do so and prevent numerous memory transfers for each of thefeeds 805, at 808, the data driver 120 generates a composite 810 of thefeeds 805. To allocate memory to store the composite 810, the datadriver 120 may request the memory from a host-side pinned memory pool820 (at 812). Once allocated, the data driver 120 can generate thecomposite 810 of the feeds 805, e.g., using a bin-packing algorithmwhere the largest feeds are packed before the smaller feeds.

In one embodiment, the data driver 120 initiates a transfer of a copy ofthe composite 810 to the device-side memory. The GPU 306 may allocatememory from one of the GPU memory pools 820. The data driver 120 thentransfers the composite copy 815 to the device-side memory allocatedfrom the GPU memory pool 820 (at 813). As a result, the data driver 120may process the feeds 805 in parallel between the host-side and thedevice-side of the server system 115. Illustratively, the data driver120 processes host-side data per feed, and processes device-side dataper feed within the composite copy 815. After the processes are complete(and output to the sensory memory 215), the data driver 120 may checkthe allocated memory back to the pinned memory pool 820 and GPU memorypool 825.

FIG. 9 illustrates a method 900 for preparing a composite of multiplefeeds of data for transfer between host and device, according to oneembodiment. As shown, method 900 begins at step 905, where the datadriver 120 receives, from the data providers 420, one or more data feeds(e.g., video feeds) to be processed. At step 910, the data driver 120packages the data into a composite. To do so, the data driver 120 mayperform a bin-packing algorithm to fit the data feeds into a chunk ofmemory allocated from a memory pool on host-side. Further, the datadriver 120 allocates a memory chunk on host-side and device-side thatcan contain the composite.

At step 915, the data driver 120 transfers a copy of the composite datato the device-side. At step 920, the data driver 120 processes thecomposite data at host-side and device-side. As stated, at host-side,the data driver 120 may process the feeds separately, while atdevice-side, the data driver 120 processes the feeds within thecomposite. Once the feeds are processed, the data driver 120 may outputthe resulting sample data to the sensory memory 215. At step 925, thedata driver 120 releases the memory chunks previously storing thecomposite data to the respective memory pools. The memory chunks maythereafter be checked out for incoming input data feeds as needed.

In the preceding, reference is made to embodiments of the presentdisclosure. However, the present disclosure is not limited to specificdescribed embodiments. Instead, any combination of the followingfeatures and elements, whether related to different embodiments or not,is contemplated to implement and practice the techniques presentedherein.

Furthermore, although embodiments of the present disclosure may achieveadvantages over other possible solutions and/or over the prior art,whether or not a particular advantage is achieved by a given embodimentis not limiting of the present disclosure. Thus, the following aspects,features, embodiments and advantages are merely illustrative and are notconsidered elements or limitations of the appended claims except whereexplicitly recited in a claim(s).

Aspects presented herein may be embodied as a system, method or computerprogram product. Accordingly, aspects of the present disclosure may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples a computer readable storage medium include: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the current context, acomputer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus or device.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality and operation of possible implementations ofsystems, methods and computer program products according to variousembodiments presented herein. In this regard, each block in theflowchart or block diagrams may represent a module, segment or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations the functions noted in the block may occur out of theorder noted in the figures.

For example, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. Each blockof the block diagrams and/or flowchart illustrations, and combinationsof blocks in the block diagrams and/or flowchart illustrations can beimplemented by special-purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

Embodiments presented herein may be provided to end users through acloud computing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A computer-implemented method comprising:receiving, from at least one data provider, a plurality of video feeds,at least one video feed of the plurality of video feeds having at leastone of a resolution and a frame rate that is different relative to othervideo feeds of the plurality of video feeds; allocating, from a devicememory, at least one chunk of memory from one or more variable-sizedchunks of available memory of a memory pool; packaging the plurality ofvideo feeds as a composite of the plurality of video feeds into the atleast one allocated chunk of memory of the memory pool; and transferringthe packaged composite of the plurality of video feeds to amachine-learning engine, the machine-learning engine including aneuro-linguistic model configured to determine anomalous behavior in theplurality of video feeds.
 2. The method of claim 1, wherein allocatingat least one chunk of memory from one or more variable-sized chunks ofavailable memory includes determining that at least one chunk of memoryof the memory pool is not available.
 3. The method of claim 1, furthercomprising: returning the at least one allocated chunk of memory to thememory pool after the packaged composite of the plurality of video feedshas been transferred.
 4. The method of claim 1, wherein the at least oneallocated chunk of memory is associated with a time decay constant. 5.The method of claim 4, further comprising: releasing the at least oneallocated chunk of memory after the at least one allocated chunk isunused after a specified amount of time based on the time decayconstant.
 6. The method of claim 1, wherein a device associated with thedevice memory is a graphics processing unit (GPU).
 7. The method ofclaim 1, wherein the at least one allocated chunk of memory is allocatedbased on a total device memory size.
 8. A non-transitorycomputer-readable storage medium comprising: computer-executableinstructions that, when executed on at least one processor, cause the atleast one processor to: receive, from at least one data provider, aplurality of video feeds, at least one video feed of the plurality ofvideo feeds having at least one of a resolution and a frame rate that isdifferent relative to other video feeds of the plurality of video feeds;allocate, from a device memory, at least one chunk of memory from one ormore variable-sized chunks of available memory of a memory pool; packagethe plurality of video feeds as a composite of the plurality of videofeeds into the at least one allocated chunk of memory of the memorypool; and transfer the packaged composite of the plurality of videofeeds to a machine-learning engine, the machine-learning engineincluding a neuro-linguistic model configured to determine anomalousbehavior in the plurality of video feeds.
 9. The non-transitorycomputer-readable storage medium of claim 8, wherein thecomputer-executable instructions to allocate the at least one chunk ofmemory include instructions to: determine that at least one chunk ofmemory of the memory pool is not available for allocation.
 10. Thenon-transitory computer-readable storage medium of claim 8, wherein thecomputer-executable instructions further comprise instructions thatcause the at least one processor to: return the at least one allocatedchunk of memory to the memory pool.
 11. The non-transitorycomputer-readable storage medium of claim 8, wherein the at least onechunk of allocated memory is associated with a time decay constant. 12.The non-transitory computer-readable storage medium of claim 11, whereinthe computer-executable instructions further comprise instructions thatcause the at least one processor to: release the at least one allocatedchunk of memory if the at least one allocated chunk of memory is unusedafter a specified amount of time based on the time decay constant. 13.The non-transitory computer-readable storage medium of claim 8, whereinthe device memory is associated with a graphics processing unit (GPU).14. The non-transitory computer-readable storage medium of claim 8,wherein the at least one allocated chunk of memory is allocated based ona total device memory size.
 15. A system, comprising: a processor; and amemory in communication with the processor and storing code, which, whenexecuted on the processor, causes the processor to: receive a pluralityof video feeds, at least one video feed of the plurality of video feedshaving at least one of a resolution and a frame rate that is differentrelative to other video feeds of the plurality of video feeds, allocateat least one chunk of memory from one or more variable-sized chunks ofavailable memory of a memory pool, package the plurality of video feedsas a composite of the plurality of video feeds into the at least oneallocated chunk of memory, and transfer a copy of the packaged compositeof the plurality of video feeds to a machine-learning engine including aneuro-linguistic model configured to determine anomalous behavior in theplurality of video feeds.
 16. The system of claim 15, wherein the codeto allocate the at least one chunk of memory includes code to: determineif at least one chunk of memory of the memory pool is not available forallocation.
 17. The system of claim 15, wherein the code, when executed,further causes the processor to: return the at least one allocated chunkof memory to the memory pool.
 18. The system of claim 15, wherein the atleast one chunk of allocated memory is associated with a time decayconstant.
 19. The system of claim 18, wherein the code, when executed,further causes the processor to: release the at least one allocatedchunk of memory if the at least one allocated chunk of memory is unusedafter a specified amount of time based on the time decay constant. 20.The system of claim 15, further comprising a graphics processing unit(GPU).